NSF BSF: CCF Core: AF: Small: Quantifying the Value of Bits of Information from an Algorithmic Game Theory Perspective

This project explores how access to information, whether limited or substantial, can influence the design and outcomes of marketplaces. In many real-world economic settings, from online platforms to resource allocation, decision makers often rely on incomplete or costly information. This research aims to rigorously quantify the benefit of obtaining additional information in strategic environments such as auctions, and to understand when and how acquiring information improves outcomes like efficiency or revenue. The findings will offer guidance for designing better and more informed economic mechanisms. As part of the project, educational outreach efforts will be undertaken to increase student interest and involvement in computer science and economics. To achieve these goals, the investigators develop a formal framework for quantifying the value of information in algorithmic mechanism design. The project examines how different amounts and types of information affect optimal mechanism performance in strategic settings. Central to the analysis is the study of information structures, defined as partitions of type spaces induced by acquired information, and their influence on objectives such as revenue and welfare. The research investigates mechanisms under informational constraints, evaluates tradeoffs between acquiring information and expanding market participation, and extends the approach to settings beyond auctions, including bilateral trade and optimal stopping problems. The project contributes to a deeper theoretical understanding of how information shapes mechanism design when mechanisms must remain simple, incentive compatible, and robust to uncertainty. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.